Automating material image analysis for material discovery Academic Article

abstract

• Copyright Materials Research Society 2019. Advancements in temporal and spatial resolutions of microscopes promise to expand the frontiers of understanding in materials science. Imaging techniques produce images at a high-frame rate, streaming out a tremendous amount of data. Analysis of all these images is time-consuming and labor intensive, creating a bottleneck in material discovery that needs to be overcome. This paper summarizes recent progresses in machine learning and data science for expediting and automating material image analysis. The discussion covers both static image and dynamic image analyses, followed by remarks concerning ongoing efforts and future needs in automated image analysis that accelerates material discovery.

published proceedings

• MRS COMMUNICATIONS

author list (cited authors)

• Park, C., & Ding, Y. u.

citation count

• 19

complete list of authors

• Park, Chiwoo||Ding, Yu

publication date

• January 2019

publisher

• Springer Nature  Publisher

published in

• MRS Communications  Journal

keywords

• Generic Health Relevance

Digital Object Identifier (DOI)

• 10.1557/mrc.2019.48

start page

• 545

end page

• 555

volume

• 9

issue

• 2

URL

• http://dx.doi.org/10.1557/mrc.2019.48